IHP 640 Module 9 Final Project Example

From 54% to 76%: Improving Operating Room Efficiency at Highland Valley Medical Center

[Student Name]

Southern New Hampshire University

IHP 640: Measurement, Analysis, & Models for Performance Improvement

Module Nine Final Project

[Instructor Name]

[Date]

The organization, setting and figures below are a composite written as a model document. No real employer, client, colleague or patient is described.

From 54% to 76%: Improving Operating Room Efficiency at Highland Valley Medical Center

Highland Valley Medical Center's perioperative executive committee is the audience for this report. It describes a year-long effort to reduce late starts, prolonged turnovers and overtime in the main surgical suite, from redefining the metrics to analyzing causes, modeling capacity, testing changes and building a control plan, and reports six months of results.

Summary

At baseline, 54% of first cases started on time, 13% of turnovers exceeded an hour and rooms averaged 0.8 hours of daily overtime costing about $2.1 million a year. After changes addressing preoperative readiness, surgeon and anesthesia timeliness, schedule accuracy, turnover and recovery flow, the on-time rate reached 76%, prolonged turnovers fell to 8% and overtime fell 24%, saving about $500,000 annualized. Same-day cancellations did not rise. Results fall slightly short of the aim, and the report recommends two refinements.

Measurement Foundations

The effort began by fixing definitions. Three departments had reported different on-time rates from the same data. Macario (2006) proposed judging operating room efficiency with a balanced set of indicators rather than a single measure, and the suite adopted five, covering late starts, turnovers, long turnovers, recovery delays and staffed-time efficiency, each defined with its timestamp, population and exclusions. An observation audit found unreliable exit timestamps, which were corrected before baseline data were finalized.

What the Analysis Found

A stable year of baseline data showed a system problem rather than a run of bad months. Three causes, patients not ready, surgeons arriving late and incomplete anesthesia evaluations, accounted for 70% of late first cases. Logistic regression showed that switching surgeon or service between cases, large instrument sets and short afternoon cleaning staff predicted prolonged turnovers. Monday cases and orthopedic cases ran late most often.

What the Model Found

A queueing estimate showed the recovery unit running near 83% utilization at the midday peak, where delays climb steeply, and a validated simulation reproduced observed holds. Marcon and Dexter (2006) had shown that the order of cases affects recovery unit staffing needs, and the simulation confirmed that resequencing cases, combined with faster discharge criteria, could cut holds from 1.3 to about 0.4 per day without new staff.

How the Work Was Organized

Following the approach Cima et al. (2011) described at a large academic center, the effort ran as four clinician-led work streams, one each for mornings, punctuality, room changeovers and post-surgical flow. A steering group met monthly, and each stream tested changes in small pilots before spreading them.

Changes Made

The suite introduced a document check two working days before surgery, with a Friday check for Monday cases; monthly surgeon tardiness reports; day-before anesthesia evaluations for first cases; schedule durations based on each surgeon's history; grouping of cases by service, staging of instrument sets and an added afternoon cleaning position; and resequencing of cases with new recovery discharge criteria. Wachtel and Dexter (2009) found that adjusting the schedule, including better duration estimates, reduces tardiness through the day, supporting the scheduling change.

Results

Six months after full rollout, the first-case on-time rate averaged 76% and tardiness 4.8 minutes. The weekly p chart showed eight consecutive points above the old center line within the first six weeks and several points above the old upper limit, confirming a special-cause shift. Prolonged turnovers fell to 8%, recovery holds averaged 0.5 per day and overtime fell from 0.8 to 0.61 hours per room per day.

Table 1. Baseline and Six-Month Results

Note. Composite data; six-month values are averages after full rollout.

What Worked and What Fell Short

The readiness check produced the largest gain, cutting readiness delays by more than half suite-wide. Anesthesia evaluations the day before also worked well. Surgeon feedback helped, but four surgeons account for most remaining late arrivals. Case grouping was limited by fixed block assignments, which kept prolonged turnovers above the 6% target.

Financial Results

Overtime fell from about 2,800 to about 2,140 hours a year on an annualized basis, saving roughly $500,000 in premium pay. Against this, the suite added an afternoon cleaning position costing about $52,000 and absorbed roughly $40,000 in preoperative nursing time, leaving net savings of about $410,000. Time recovered from fewer holds and delays has also allowed the scheduling office to add roughly two cases a week in orthopedics, which is not counted in the savings above but improves contribution margin and surgeon satisfaction.

Unintended Effects

Preoperative nurses absorbed about twenty minutes of added work per day, funded from overtime savings. One unexpected benefit emerged: earlier anesthesia evaluations identified patients needing additional testing days before surgery rather than on the morning, which may explain why same-day cancellations dipped slightly.

Control and Sustainment

Fresh control limits were calculated once a quarter of steady post-rollout data had accumulated. Each measure has an owner, a chart and a response rule: any signal sends the responsible work stream back to investigate inside a fortnight. Changes are embedded in preoperative policy, the scheduling system, sterile processing standard work and the budget.

Recommendations

Two refinements should close the remaining gap. First, the surgical chief should meet individually with the four surgeons responsible for most late arrivals and, if needed, adjust their first-case assignments. Second, the perioperative committee should revise block rules to allow case grouping by service within shared rooms. If recovery holds exceed 0.5 per day for two months, a midday recovery nurse should be added.

Limitations

Six months is a short follow-up, and some gains may reflect staff attention during a visible project. Savings are estimated from overtime hours and do not include possible added cases. The analysis did not include the ambulatory surgery unit or obstetric rooms.

Conclusion

Precise measurement, data analysis, capacity modeling and front-line work streams moved Highland Valley's surgical suite from a stable 54% to 76% on-time first cases and cut overtime by nearly a quarter. The remaining gap is narrow and concentrated, and the control plan gives the suite the structure to hold what it has gained while closing it.

References

Cima, R. R., Brown, M. J., Hebl, J. R., Moore, R., Rogers, J. C., Kollengode, A., Amstutz, G. J., Weisbrod, C. A., Narr, B. J., & Deschamps, C. (2011). Use of Lean and Six Sigma methodology to improve operating room efficiency in a high-volume tertiary-care academic medical center. Journal of the American College of Surgeons, 213(1), 83-92. https://doi.org/10.1016/j.jamcollsurg.2011.02.009

Macario, A. (2006). Are your hospital operating rooms "efficient"? A scoring system with eight performance indicators. Anesthesiology, 105(2), 237-240. https://doi.org/10.1097/00000542-200608000-00004

Marcon, E., & Dexter, F. (2006). Impact of surgical sequencing on post anesthesia care unit staffing. Health Care Management Science, 9(1), 87-98. https://doi.org/10.1007/s10729-006-6282-x

Wachtel, R. E., & Dexter, F. (2009). Reducing tardiness from scheduled start times by making adjustments to the operating room schedule. Anesthesia & Analgesia, 108(6), 1902-1909. https://doi.org/10.1213/ane.0b013e31819f9fd2